Saliency Detection is a preprocessing step in computer vision which aims at finding salient objects in an image.
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The explicitly extracted edge information goes together with saliency to give more emphasis to the salient regions and object boundaries.
We design a real-time portrait matting pipeline for everyday use, particularly for "virtual backgrounds" in video conferences.
However, since generative models are known to be unstable and sensitive to hyperparameters, the training of these methods can be challenging and time-consuming.
Specifically, our decoder consists of two branches, a saliency branch and a contour branch.
Considering the reliability of the other modality's attention, we further propose a selection attention to weight the newly added attention term.
Existing state-of-the-art RGB-D salient object detection methods explore RGB-D data relying on a two-stream architecture, in which an independent subnetwork is required to process depth data.
I suspect that the classification of an object is strongly influenced by the background pixels on which the object is located.
To address the second challenge, we propose an Attention-based Multi-level Integrator Module to give the model the ability to assign different weights to multi-level feature maps.
In this paper, we corroborate based on three subjective experiments on a novel image dataset that objects in natural images are inherently perceived to have varying levels of importance.
Our experiment shows FN-AUC can measure spatial biases, central and peripheral, more effectively than S-AUC without penalizing the fixation locations.